In modern data processing, in particular in video and audio applications, neural networks are playing an increasing role. The fundamental elements of these neural networks are neurons, in which, in the simplest case, a number of inputs act with variable weight on a single output, the signal of the latter respectively assuming a different logic state if the weighted sum of all inputs exceeds or falls below a specific threshold. Neural networks have some similarities with brains, since, in particular, they are capable of learning and the quality of their results can be improved constantly by repeated practice. At present, neurons are for the most part produced using software. However, for faster and more complex applications of neural networks, it is in many cases necessary to implement neurons using hardware, but this has so far raised problems in adaptability to a variety of applications. Applications which are further aspired to, for example in image processing, require very high processing speeds, which in practice can only be achieved using hardware solutions.
IEEE Transactions on Electron Devices, Vol. 39, No. 6, June 1992, pages 1444 to 1454, has already disclosed a component which, with a very small surface area, has important partial functions, for example the logical coupling of a plurality of inputs, and the formation of a threshold value. A disadvantage in this case is that the individual inputs cannot be weighted differently, or that the weighting of the inputs cannot be altered flexibly.
European patent applications 0 685 808 A and 0 657 934 A respectively disclose a semiconductor neuron, whereby an input electrode comprising a plurality of sub-electrodes is capacitatively coupled to a floating gate, whereby the inputs of the semiconductor neuron can be connected to a plurality of sub-electrodes, so that, for the overall area of these inputs, the weightings of all neuron inputs can be modified. In FIG. 8 of the 0 685 808 A application, for example, the input Ci-1 is connected to two electrodes of the two last stages, whereas the two other signal inputs are only respectively connected to one electrode. In FIG. 1c of the 0 657 934 A application, for example, the input electrodes A1-A3 are connected to one another, whereas the input electrodes C1-C4 are connected to one another.